Papers by Francesco Trebbi
Improving Context Modeling in Neural Topic Segmentation (2020.aacl-main)
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| Challenge: | Recent work favors highly effective neural supervised approaches for topic segmentation but current neural solutions are limited in how they model context. |
| Approach: | They propose to enhance a hierarchical attention biLSTM network-based topic segmenter to better model context by adding a coherence-related auxiliary task and restricted self-attention. |
| Outcome: | The proposed model outperforms SOTA approaches on three datasets and on four real-world benchmarks. |